Differential phospho analysis - CRC Batch 2 Set 5 (normXenograft1)
Sophie Herbst
Analysis date: 2023-08-08
Depends on
CRC_Xenografts_Batch2_DataProcessing Script
load("../Data/Cache/Xenografts_Batch2_DataProcessing.RData")TODO
Setup
Load libraries and functions
Analysis
DEP
Tyrosine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pY <- test_diff(pY_se_Set5_normXenograft1, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pY <- add_rejections_SH(data_diff_ctrl_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pY, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_ctrl_vs_E_pY, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns## Loading required namespace: reactome.db## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.687622790
## 2: ABC transporter disorders 0.082329317
## 3: ABC-family proteins mediated transport 0.082329317
## 4: ADP signalling through P2Y purinoceptor 1 0.470695971
## 5: ALK mutants bind TKIs 0.199633700
## 6: APC/C-mediated degradation of cell cycle proteins 0.006884729
## padj log2err ES NES size leadingEdge
## 1: 0.8538066 0.06224904 -0.6652542 -0.8901041 1 6385
## 2: 0.3633962 0.22205605 0.9661017 1.2978731 1 5692
## 3: 0.3633962 0.22205605 0.9661017 1.2978731 1 5692
## 4: 0.7346950 0.07767986 0.6324772 0.9994649 2 6714
## 5: 0.5140811 0.13077714 0.7582498 1.1982157 2 1213,27436
## 6: 0.1853547 0.40701792 0.9702128 1.5331678 2 983,5692data_diff_EC_vs_ctrl_pY <- test_diff(pY_se_Set5_normXenograft1, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pY <- add_rejections_SH(data_diff_EC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pY, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pY") Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.92190476
## 2: ABC transporter disorders 0.14095238
## 3: ABC-family proteins mediated transport 0.14095238
## 4: ADP signalling through P2Y purinoceptor 1 0.59756098
## 5: ALK mutants bind TKIs 0.94757282
## 6: APC/C-mediated degradation of cell cycle proteins 0.02022139
## padj log2err ES NES size leadingEdge
## 1: 0.9630818 0.04716425 0.5466102 0.7322796 1 6385
## 2: 0.7474717 0.16197895 0.9364407 1.2545256 1 5692
## 3: 0.7474717 0.16197895 0.9364407 1.2545256 1 5692
## 4: 0.9070775 0.07078991 0.6035936 0.9539074 2 6714
## 5: 0.9767694 0.04678830 -0.3829787 -0.5812824 2 27436,1213
## 6: 0.3959715 0.35248786 0.9404255 1.4862298 2 983,5692## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pY, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pY <- test_diff(pY_se_Set5_normXenograft1, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pY <- add_rejections_SH(data_diff_EBC_vs_ctrl_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pY, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_EBC_vs_ctrl_pY, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.7737374
## 2: ABC transporter disorders 0.3393939
## 3: ABC-family proteins mediated transport 0.3393939
## 4: ADP signalling through P2Y purinoceptor 1 0.1475096
## 5: ALK mutants bind TKIs 0.4731801
## 6: APC/C-mediated degradation of cell cycle proteins 0.1264368
## padj log2err ES NES size leadingEdge
## 1: 0.8799526 0.05797548 0.6144068 0.8169759 1 6385
## 2: 0.6621871 0.10171390 0.8474576 1.1268634 1 5692
## 3: 0.6621871 0.10171390 0.8474576 1.1268634 1 5692
## 4: 0.6621871 0.15851411 0.8314748 1.2720413 2 6714,1432
## 5: 0.7686500 0.07977059 0.6654134 1.0179904 2 1213,27436
## 6: 0.6321336 0.17232434 0.8510638 1.3020099 2 983,5692
## Note: Row-scaling applied for this heatmap
EC vs E
data_diff_EC_vs_E_pY <- test_diff(pY_se_Set5_normXenograft1, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pY <- add_rejections_SH(data_diff_EC_vs_E_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pY, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pY", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_EC_vs_E_pY, comparison = "EC_vs_E_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.5928854
## 2: ABC transporter disorders 0.9841897
## 3: ABC-family proteins mediated transport 0.9841897
## 4: ADP signalling through P2Y purinoceptor 1 0.6348774
## 5: ALK mutants bind TKIs 0.7735849
## 6: APC/C-mediated degradation of cell cycle proteins 0.2697548
## padj log2err ES NES size leadingEdge
## 1: 0.8872970 0.06977925 0.7033898 0.9484105 1 6385
## 2: 0.9918233 0.04586203 0.5084746 0.6855979 1 5692
## 3: 0.9918233 0.04586203 0.5084746 0.6855979 1 5692
## 4: 0.9232023 0.08289621 0.5446809 0.8850280 2 1432,6714
## 5: 0.9918233 0.04641550 -0.5446809 -0.8108519 2 1213,27436
## 6: 0.7532999 0.13802224 0.7148006 1.1614481 2 983,5692#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pY <- test_diff(pY_se_Set5_normXenograft1, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pY <- add_rejections_SH(data_diff_EBC_vs_EC_pY, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pY, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pY")Return_DEP_Hits_Plots(data = pY_Set5_normXenograft1_form, dep_EBC_vs_EC_pY, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:many mapping between keys and columns
## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns
## pathway pval
## 1: A tetrasaccharide linker sequence is required for GAG synthesis 0.9866920
## 2: ABC transporter disorders 0.5019011
## 3: ABC-family proteins mediated transport 0.5019011
## 4: ADP signalling through P2Y purinoceptor 1 0.7764706
## 5: ALK mutants bind TKIs 0.3831933
## 6: APC/C-mediated degradation of cell cycle proteins 0.3235294
## padj log2err ES NES size leadingEdge
## 1: 0.9950068 0.04397593 -0.5042373 -0.6796597 1 6385
## 2: 0.8781315 0.07627972 -0.7457627 -1.0052110 1 5692
## 3: 0.8781315 0.07627972 -0.7457627 -1.0052110 1 5692
## 4: 0.9316552 0.04929177 0.5182433 0.8067578 2 1432,6714
## 5: 0.8781315 0.08407456 0.6936170 1.0797651 2 1213,27436
## 6: 0.8781315 0.11724972 -0.7489362 -1.1611361 2 983,5692#data_results <- get_df_long(dep)Serine/Threonine all
Each condition vs ctrl
data_diff_ctrl_vs_E_pST <- test_diff(pST_se_Set5_normXenograft1, type="manual", test = "E_vs_ctrl")## Tested contrasts: E_vs_ctrldep_ctrl_vs_E_pST <- add_rejections_SH(data_diff_ctrl_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_ctrl_vs_E_pST, contrast = "E_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_ctrl_vs_E_pST, comparison = "E_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.6282051 0.9910908
## 2: ABC transporter disorders 0.2564103 0.9910908
## 3: ABC-family proteins mediated transport 0.2583082 0.9910908
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.8569501 0.9910908
## 5: AKT phosphorylates targets in the cytosol 0.9335347 0.9910908
## 6: ALK mutants bind TKIs 0.3280423 0.9910908
## log2err ES NES size leadingEdge
## 1: 0.07078991 -0.7104377 -0.9339203 1 3159
## 2: 0.12384217 -0.8754209 -1.1508023 1 5684
## 3: 0.10063339 -0.7991094 -1.1640850 2 5684
## 4: 0.03464102 -0.4660188 -0.7368427 3 5577
## 5: 0.03592087 -0.4712812 -0.6865285 2 84335
## 6: 0.17821987 0.4991540 1.0810484 4 6801,5573
## Warning: we couldn't map to STRING 2% of your identifiers## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
data_diff_EC_vs_ctrl_pST <- test_diff(pST_se_Set5_normXenograft1, type="manual", test = "EC_vs_ctrl")## Tested contrasts: EC_vs_ctrldep_EC_vs_ctrl_pST <- add_rejections_SH(data_diff_EC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_ctrl_pST, contrast = "EC_vs_ctrl",
add_names = TRUE,
additional_title = "pST") Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.9960000 0.9998561
## 2: ABC transporter disorders 0.3127490 0.9443669
## 3: ABC-family proteins mediated transport 0.6971609 0.9751725
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.7311475 0.9751725
## 5: AKT phosphorylates targets in the cytosol 0.3990536 0.9751725
## 6: ALK mutants bind TKIs 0.7148760 0.9751725
## log2err ES NES size leadingEdge
## 1: 0.04586203 0.5050505 0.6656025 1 3159
## 2: 0.10592029 -0.8417508 -1.1215029 1 5684
## 3: 0.05132233 -0.5961474 -0.8847886 2 5684
## 4: 0.08528847 0.4324324 0.7756898 3 5576,5573
## 5: 0.07850290 -0.7079430 -1.0507131 2 84335
## 6: 0.09992770 0.3976311 0.7916561 4 6801,4869,5573
## Warning: we couldn't map to STRING 6% of your identifiers
## Note: Row-scaling applied for this heatmap
Plot_Enrichment_Single_Pathway(dep_EC_vs_ctrl_pST, comparison = "EC_vs_ctrl_diff",
pw = "Epigenetic regulation of gene expression")data_diff_EBC_vs_ctrl_pST <- test_diff(pST_se_Set5_normXenograft1, type="manual", test = "EBC_vs_ctrl")## Tested contrasts: EBC_vs_ctrldep_EBC_vs_ctrl_pST <- add_rejections_SH(data_diff_EBC_vs_ctrl_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_ctrl_pST, contrast = "EBC_vs_ctrl",
add_names = TRUE,
additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_EBC_vs_ctrl_pST, comparison = "EBC_vs_ctrl_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.6164659 1.0000000
## 2: ABC transporter disorders 0.2369478 0.9027377
## 3: ABC-family proteins mediated transport 0.1959565 0.9027377
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.1944444 0.9027377
## 5: AKT phosphorylates targets in the cytosol 0.7231726 1.0000000
## 6: ALK mutants bind TKIs 0.6550218 1.0000000
## log2err ES NES size leadingEdge
## 1: 0.06863256 -0.7037037 -0.9396615 1 3159
## 2: 0.12503337 -0.8787879 -1.1734529 1 5684
## 3: 0.12043337 -0.8291864 -1.2175548 2 5684
## 4: 0.18820415 0.6672297 1.2424457 3 5576,5573,5577
## 5: 0.04899541 -0.5800838 -0.8517793 2 84335
## 6: 0.10882013 0.4094755 0.8725818 4 6801,5573## Warning in max(screen_pval05_pos[, logFcColStr]): no non-missing arguments to
## max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
EC vs E
data_diff_EC_vs_E_pST <- test_diff(pST_se_Set5_normXenograft1, type = "manual",
test = c("EC_vs_E"))## Tested contrasts: EC_vs_Edep_EC_vs_E_pST <- add_rejections_SH(data_diff_EC_vs_E_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EC_vs_E_pST, contrast = "EC_vs_E", add_names = TRUE, additional_title = "pST", proteins_of_interest = "EGFR")Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_EC_vs_E_pST, comparison = "EC_vs_E_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.07768924 0.8676415
## 2: ABC transporter disorders 0.60358566 0.9661613
## 3: ABC-family proteins mediated transport 0.38157895 0.9661613
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.94736842 0.9827926
## 5: AKT phosphorylates targets in the cytosol 0.01218565 0.6932841
## 6: ALK mutants bind TKIs 0.72527473 0.9661613
## log2err ES NES size leadingEdge
## 1: 0.22798720 0.9612795 1.3013697 1 3159
## 2: 0.06928365 0.6919192 0.9367127 1 5684
## 3: 0.08312913 0.6930860 1.0695220 2 23,5684
## 4: 0.07182763 -0.3496622 -0.6500324 3 5573,5577,5576
## 5: 0.38073040 -0.9537873 -1.5623729 2 84335
## 6: 0.04319020 0.4331583 0.8193379 4 4869,5573,6801,27436## Warning in min(screen_pval05_neg[, logFcColStr]): no non-missing arguments to
## min; returning Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(x): no non-missing arguments to min; returning Inf## Warning in max(x): no non-missing arguments to max; returning -Inf## Warning in min(cs1s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs1s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs2s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs2s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf## Warning in min(cs3s, na.rm = TRUE): no non-missing arguments to min; returning
## Inf## Warning in max(cs3s, na.rm = TRUE): no non-missing arguments to max; returning
## -Inf
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)EBC vs EC
data_diff_EBC_vs_EC_pST <- test_diff(pST_se_Set5_normXenograft1, type = "manual",
test = c("EBC_vs_EC"))## Tested contrasts: EBC_vs_ECdep_EBC_vs_EC_pST <- add_rejections_SH(data_diff_EBC_vs_EC_pST, alpha = 0.05, lfc = log2(1.2))
GGPlotly_Volcano(dep_EBC_vs_EC_pST, contrast = "EBC_vs_EC", add_names = TRUE, additional_title = "pST")Return_DEP_Hits_Plots(data = pST_Set5_normXenograft1_form, dep_EBC_vs_EC_pST, comparison = "EBC_vs_EC_diff")## 'select()' returned 1:1 mapping between keys and columns## 'select()' returned 1:many mapping between keys and columns## 'select()' returned 1:1 mapping between keys and columns## pathway pval padj
## 1: 2-LTR circle formation 0.1769384 0.9955648
## 2: ABC transporter disorders 0.4791252 0.9955648
## 3: ABC-family proteins mediated transport 0.2563238 0.9955648
## 4: ADORA2B mediated anti-inflammatory cytokines production 0.5090909 0.9955648
## 5: AKT phosphorylates targets in the cytosol 0.2738386 0.9955648
## 6: ALK mutants bind TKIs 0.9284627 0.9955648
## log2err ES NES size leadingEdge
## 1: 0.14641624 -0.9259259 -1.2217139 1 3159
## 2: 0.08108021 -0.7794613 -1.0284609 1 5684
## 3: 0.10797236 -0.7807757 -1.1745956 2 23,5684
## 4: 0.09255289 0.5214048 0.9310801 3 5577,5573
## 5: 0.12878871 0.7707644 1.1995212 2 84335,572
## 6: 0.03653149 -0.3447187 -0.6024988 4 4869
## Note: Row-scaling applied for this heatmap
#data_results <- get_df_long(dep)Session Info
sessionInfo()## R version 4.2.3 (2023-03-15)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] lubridate_1.9.2 forcats_1.0.0
## [3] stringr_1.5.0 dplyr_1.1.2
## [5] purrr_1.0.1 readr_2.1.4
## [7] tidyr_1.3.0 tibble_3.2.1
## [9] ggplot2_3.4.2 tidyverse_2.0.0
## [11] mdatools_0.14.0 SummarizedExperiment_1.28.0
## [13] GenomicRanges_1.50.2 GenomeInfoDb_1.34.9
## [15] MatrixGenerics_1.10.0 matrixStats_1.0.0
## [17] DEP_1.20.0 org.Hs.eg.db_3.16.0
## [19] AnnotationDbi_1.60.2 IRanges_2.32.0
## [21] S4Vectors_0.36.2 Biobase_2.58.0
## [23] BiocGenerics_0.44.0 fgsea_1.24.0
##
## loaded via a namespace (and not attached):
## [1] circlize_0.4.15 fastmatch_1.1-3 plyr_1.8.8
## [4] igraph_1.5.0.1 gmm_1.8 lazyeval_0.2.2
## [7] shinydashboard_0.7.2 crosstalk_1.2.0 BiocParallel_1.32.6
## [10] digest_0.6.33 foreach_1.5.2 htmltools_0.5.5
## [13] fansi_1.0.4 magrittr_2.0.3 memoise_2.0.1
## [16] cluster_2.1.4 doParallel_1.0.17 tzdb_0.4.0
## [19] limma_3.54.2 ComplexHeatmap_2.14.0 Biostrings_2.66.0
## [22] imputeLCMD_2.1 sandwich_3.0-2 timechange_0.2.0
## [25] colorspace_2.1-0 blob_1.2.4 xfun_0.39
## [28] crayon_1.5.2 RCurl_1.98-1.12 jsonlite_1.8.7
## [31] impute_1.72.3 zoo_1.8-12 iterators_1.0.14
## [34] glue_1.6.2 hash_2.2.6.2 gtable_0.3.3
## [37] zlibbioc_1.44.0 XVector_0.38.0 GetoptLong_1.0.5
## [40] DelayedArray_0.24.0 shape_1.4.6 scales_1.2.1
## [43] pheatmap_1.0.12 vsn_3.66.0 mvtnorm_1.2-2
## [46] DBI_1.1.3 Rcpp_1.0.11 plotrix_3.8-2
## [49] mzR_2.32.0 viridisLite_0.4.2 xtable_1.8-4
## [52] clue_0.3-64 reactome.db_1.82.0 bit_4.0.5
## [55] preprocessCore_1.60.2 sqldf_0.4-11 MsCoreUtils_1.10.0
## [58] DT_0.28 htmlwidgets_1.6.2 httr_1.4.6
## [61] gplots_3.1.3 RColorBrewer_1.1-3 ellipsis_0.3.2
## [64] farver_2.1.1 pkgconfig_2.0.3 XML_3.99-0.14
## [67] sass_0.4.7 utf8_1.2.3 STRINGdb_2.10.1
## [70] labeling_0.4.2 tidyselect_1.2.0 rlang_1.1.1
## [73] later_1.3.1 munsell_0.5.0 tools_4.2.3
## [76] cachem_1.0.8 cli_3.6.1 gsubfn_0.7
## [79] generics_0.1.3 RSQLite_2.3.1 fdrtool_1.2.17
## [82] evaluate_0.21 fastmap_1.1.1 mzID_1.36.0
## [85] yaml_2.3.7 knitr_1.43 bit64_4.0.5
## [88] caTools_1.18.2 KEGGREST_1.38.0 ncdf4_1.21
## [91] mime_0.12 compiler_4.2.3 rstudioapi_0.15.0
## [94] plotly_4.10.2 png_0.1-8 affyio_1.68.0
## [97] stringi_1.7.12 bslib_0.5.0 highr_0.10
## [100] MSnbase_2.24.2 lattice_0.21-8 ProtGenerics_1.30.0
## [103] Matrix_1.6-0 tmvtnorm_1.5 vctrs_0.6.3
## [106] pillar_1.9.0 norm_1.0-11.1 lifecycle_1.0.3
## [109] BiocManager_1.30.21.1 jquerylib_0.1.4 MALDIquant_1.22.1
## [112] GlobalOptions_0.1.2 data.table_1.14.8 cowplot_1.1.1
## [115] bitops_1.0-7 httpuv_1.6.11 R6_2.5.1
## [118] pcaMethods_1.90.0 affy_1.76.0 promises_1.2.0.1
## [121] KernSmooth_2.23-22 codetools_0.2-19 MASS_7.3-60
## [124] gtools_3.9.4 assertthat_0.2.1 chron_2.3-61
## [127] proto_1.0.0 rjson_0.2.21 withr_2.5.0
## [130] GenomeInfoDbData_1.2.9 parallel_4.2.3 hms_1.1.3
## [133] grid_4.2.3 rmarkdown_2.23 shiny_1.7.4.1knitr::knit_exit()